Resource Allocation of Uplink for Multibeam Satellite Based on MF- TDMA
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Journal of Physics: Conference Series
PAPER • OPEN ACCESS
Resource Allocation of Uplink for Multibeam Satellite Based on MF-
TDMA
To cite this article: Xiaoyan Liu et al 2021 J. Phys.: Conf. Ser. 1815 012026
View the article online for updates and enhancements.
This content was downloaded from IP address 181.215.51.193 on 12/05/2021 at 04:21CCME 2020 IOP Publishing
Journal of Physics: Conference Series 1815 (2021) 012026 doi:10.1088/1742-6596/1815/1/012026
Resource Allocation of Uplink for Multibeam Satellite Based
on MF-TDMA
Xiaoyan Liu, Linlin Duan, Kexian Gong*, Min Zhang and Qian Cheng
Electronics and Communication Engineering, College of Information Engineering,
Zhengzhou University, Henan Prov, China
*Corresponding author email: ggkx@163.com
Abstract. In this article, Flexible Joint Time Slot and Power Allocation (FTPA) algorithm
considering interference is used for system resources allocation. Combined with the actual
situation of satellite communication, the demand supply variance minimization is used as the
objective function to analyze and solve the resource allocation problem reasonably. The
Lagrangian dual and sub-gradient algorithm are used to replace the traditional method to
improve the problem, and the optimal system allocation under the current situation is obtained.
Compared with classical algorithm, FTPA guarantees the maximum capacity of the system and
the fairness between beams.
Keywords: Resource Allocation; MF-TDMA; FTPA.
1. Introduction
Satellite communication network has been widely used in the fields of communication, radio and
television, aviation, maritime affairs and mobile after half a century of development. The government
provides services for the citizens, enterprises need to meet the needs of a variety of multimedia users,
consumers are eager to enjoy a fast and smooth Internet experience, this shows that people are
increasingly relying on satellite communications. Therefore, multi-beam satellites with the advantages
of beamspace isolation and frequency multiplexing are gradually emerging in the field of
communication.
In communication of multi-beam satellite, the commonly used allocation strategies include fixed
allocation and on-demand allocation. The allocation result of fixed allocation strategies remains the
same when determined. This allocation method reduces the complexity of the system. But it causes
great waste of resources. And it cannot adapt to the dynamic changes of business demand in the actual
system. Therefore, dynamic resource allocation has become the focus of research.
The article [1] proposed an algorithm for joint allocation of power and carrier resources. Compared
with the classical fixed algorithm, the utilization of spectrum and the satisfaction of communication
have been improved. But the problem of maximizing system capacity is not considered. [2] proposed a
joint power and time slot resource allocation algorithm in satellite communication system. But the
main focus is on energy efficiency. A Lagrangian multiplier method is proposed in article [3] to
allocate the power of the system. The algorithm improves the power utilization of the satellite. But it
does not consider the issue of system capacity. [4] proposed golden section methods and sub-gradient
iteration. In this way, the channel capacity can be maximized and the variance of bandwidth utilization
can be minimized. A scheme about satellite downlink power and beam allocation based on demand
and channel conditions is proposed in [6]. In this scheme, the objective function of first order, second
order and third order difference between supply and demand is compared. The second order objective
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Published under licence by IOP Publishing Ltd 1CCME 2020 IOP Publishing
Journal of Physics: Conference Series 1815 (2021) 012026 doi:10.1088/1742-6596/1815/1/012026
function is considered to be a good compromise between throughput maximization and fairness. A
capacity calculation model based on the satellite link budget equation is established in [9]. But it only
optimizes the power resource.
In fact, power and time-frequency resources are mutually complementary and interdependent. The
joint design can increase the capacity of satellite system and reduce the payload. Despite the need for
more information exchange and joint control, the prospect remains tantalizing. Without joint allocation
of resources, it is difficult to guarantee the fairness between beams, particularly when the channel
conditions are poor. In addition, the interference of the satellite system is also increasing. These
studies have not considered the interference between beams, which is a problem that cannot be
ignored.
Therefore, this paper proposes Flexible Joint Time Slot and Power Allocation (FTPA) algorithm which
considering interference between beams and the current channel conditions. The problem was
formulated and an appropriate mathematical model was established. The Lagrangian dual and
sub-gradient iteration algorithm are used to replace the traditional method to solve the problem. The
optimal system allocation under the current situation is obtained. It ensures the maximum capacity of
the system and the fairness between the beams.
2. System Model of Multibeam Satellite
Satellite communication has two links, uplink and downlink. Uplink, also known as forward link,
refers to the process of network control center-satellite-terminal. Downlink, also known as reverse link,
refers to the process of terminal-satellite-network control center. This article uses the multi-beam
satellite uplink of the MF-TDMA (Multi-frequency Time Division Multiple Access) system as the
background to allocate satellite communication resources.
In actual satellite communication, one beam interference mainly comes from other same frequency
beams. In order to reduce the same frequency interference, there are two forms of frequency use:
partial frequency multiplexing and full frequency multiplexing.The full frequency is mostly used in
the beam hopping system. Only part of the beam is in the working state in each time slot. In order to
reduce the interference of the same frequency, the beam is multiplexed by spatial reuse. The nearest
beam is multiplexed by time isolation. When partial frequency multiplexing, the total bandwidth of the
system is divided into several equal size segments. Each beam can only use the allocated frequency
and bandwidth. In order to reduce the same frequency interference, this paper uses three-color beam
multiplexing. The nearest beam uses different frequencies to realize frequency multiplexing through
space.
Considering the interference of interbeam due to frequency multiplexing, a multi-beam satellite
communication system model can be established, which consists of a multi-beam satellite, a network
control center, N beams and satellite terminals.The satellite multi-beam communications system is
shown in figure 1 .
beam7
beam4 beam1
beam8
network beam2
beam9 beam5
control
center
beam3
beam10 beam6
Figure 1. Multi-beam satellite system model.
2CCME 2020 IOP Publishing
Journal of Physics: Conference Series 1815 (2021) 012026 doi:10.1088/1742-6596/1815/1/012026
This article uses the MF-TDMA system used more commonly in satellite uplink communications as a
background to allocate resources. For the purpose of reducing the complexity of the problem, the
carrier bandwidth is assumed to be fixed.
Assuming ptotal is the total power value of the satellite communications system, Uˆ i is the
communication service demand of the beam i , U i is the actual channel capacity allocated for the
beam i , pi is the actual power value assigned to of the beam i , N 0 is the power spectral density of all
beams noise in communication transmission, i2 is the attenuation factor of the link of the channel
where of the beam i is located. Moreover, it is necessary to consider that the beam i is affected by
side-lobe k , and the link attenuation factor is ik2 , hik represents the interference coefficient caused by
ik2
the beam k to the beam i , its formula is hik .
i2
Accordingly, the signal-to-noise ratio of the beam i can be expressed as:
i2 pi
SINRi N
N0B
k 1,k i
hik pk (1)
Then the expression of the resource allocated by the system to the beam i is:
U i Ti * B *log 2 (1 SINRi )
(2)
Considering the fair allocation of resources, the second order differential objective function is used as
its optimization problem, which is showned as follows:
N 2
min | U i Uˆ i |
pi ,Ti
i 1
(3)
subject to :
U i -Uˆ i 0
(4)
N
pi ptotal
i 1 (5)
N
T T
i 1
i total
(6)
Formula (3)-(6) describes the objective function and its constraints of minimizing the variance of
demand supply. The objective function formula(3) requires the minimum demand supply variance of
the beam. The constraint condition formula(4) indicates that the resources allocated by the system to
the beam should be as close as possible to the actual demand, but not larger than the beam demand, so
as to ensure that the distribution is fair and the resources are not wasted. The constraint condition
formula(5) indicates that the sum of power resources of all beams can not exceed the total power
resources of the system. The constraint condition formula(6) indicates that the sum of slots for all
beams can not exceed the total number of slots for the system.
3. Algorithm Description
The Lagrangian dual and sub-gradient iteration algorithm are used to solve the above problems, and
the original optimization problem is modeled by introducing a non-negative dual
variable m and l and i . The Lagrange function is defined as:
3CCME 2020 IOP Publishing
Journal of Physics: Conference Series 1815 (2021) 012026 doi:10.1088/1742-6596/1815/1/012026
N N N N
L1 (Uˆ i U i )2 i (Uˆ i U i ) m( Ti Ttotal ) l ( pi ptotal ) (7)
i 1 i 1 i 1 i 1
The problem can be solved in three steps:
Step 1: getting the time slot allocation result of each point beam. The power of each beam is set as a
certain value, and the partial derivative of the time slot Ti is obtained from the above equation (7) :
L1
2(Uˆ i U i ) i B log 2 (1 SINRi ) m (8)
Ti
Setting the partial derivative of equation (8) to 0, the following equation can be obtained.
(2Uˆ i i )(Wi log 2 (1 SINRi )) m
Ti (9)
2(Wi log 2 (1 SINRi )) 2
Through the above equation (9), the time slot value allocated by the system can be obtained. The time
slot should be a non-negative value, if its value is less than 0, we set it to 0 and set the power value of
the corresponding beam to 0.
Step 2: getting the power distribution results of each point beam. The calculated time slot values of
each beam are substituted into equation (7), and then the partial derivative of power pi of equation (7)
is obtained as follows:
N
Ti B i2 Wi N 0 pk hik pi hik
L1 ˆ
2 Ui Ui i
Pi k 1,k i
2
(10)
2 1 SINR W N
N
i i 0 pk hik )
k 1,k i
Let the partial derivative of equation (9) be 0, the following equation can be obtained.
2W T 1 WiTi 1
（Uˆ i Ui） i i * N
i ( * N
)
ln 2 ln 2
Wi N 0 pk hik i2 pi Wi N 0 pk hik i2 pi
k 1, k i k 1,k i (11)
N
WT i2 pi h jk
i i *(2Uˆ i 2Ui j )* N N
l
k 1,k i ln 2
(Wi N 0
k 1,k i
pk hik )2 pk (Wi N 0
k 1,k i
pk hik )
Through the above formula (11), the power value allocated by the system can be obtained. Since the
power should be a non-negative value, if the value is less than 0, set it to 0 and set the time slot value
of the corresponding beam to 0.
Step 3: updating dual variables iterative. The m update operator can be expressed as equation (12), the
update operato l can be expressed as equation (13), the update operator i can be expressed as equation
(14).
N
m n 1 m n nm (Ttotal T opt )
i 1 (12)
N
l n 1
l n ln ( ptotal p opt )
i 1 (13)
in 1 in n (Uˆ i U i )
(14)
4CCME 2020 IOP Publishing
Journal of Physics: Conference Series 1815 (2021) 012026 doi:10.1088/1742-6596/1815/1/012026
The update step size of the parameter Ti , pi and U i are nm , ln and n i .
The algorithm flow is shown as follows:
Updates the
Does it meet the Y Output the current
Selected system allocated
termination optimal allocation
initial value time slot and
conditions? result
power values
N
Update
corresponding
operator
Figure 2. Algorithm flow chart.
Step1: determine the values of non-negative dual variables m , l and i , set the initial power value of
each point beam;
Step2: use formula (9) to get the time slot value of each point beam;
Step3: use formula (11) to get the power value of each point beam;
Step4: use formula (12), formula(13) and formula(14) to update the nonnegative dual
variables m , l and i iteratively, if the conditions are satisfied at the same time, then the algorithm
terminates, otherwise it jumps to the Step 2 for operation.
4. Simulation and Results Analysis
4.1. Parameter Settings
The proposed FTPA algorithm considering interbeam interference is simulated and compared with the
traditional FTA( flexible time slot allocation) and FPA( flexible power allocation) performance.The
simulation was carried out using Matlab R2018a environment.
Parameter settings required for simulationare set: the relevant parameters of the multi-beam satellite
system are given in table 1. Assuming that the normalized noise spectral density coefficient N 0 i2 of
each beam is 0.2，0.25，0.3，0.35，0.4，0.2，0.2，0.2，0.2，0.2 10-6 . Considering the uneven
operational requirements between satellite uplink beams, assume that the operational demand for each
point beam is 80，90，110，110，110，130，140，150，160，170 Mbit / s .
The interference coefficient between beams are as follows:
0.3，k i 1; k i N 1
0.2，k i 2; k i N 2
hik (15)
0.1，k i 3; k i N 3
0, others
Table 1. Parameters of a multi-beam satellite system.
parameter value
Satellite altitude h 36000km
beam radius R 97.5km
Number of beams 10
Total system power 200W
Bandwidth of each beam 50MHz
Maximum number of iterations 5000
5CCME 2020 IOP Publishing
Journal of Physics: Conference Series 1815 (2021) 012026 doi:10.1088/1742-6596/1815/1/012026
4.2. Algorithm Evaluation Parameters
Total system capacity U total : the sum of the actual capacity of each beam is defined as:
K
U total U i (16)
i 1
Error between beam demand and actual distribution capacity ei : the capacity allocated to each beam
by the system and the actual demand for that beam are made second-order difference, which is defined
as:
2
ei U i Uˆ i (17)
4.3. Analysis of Results
It can be seen from figure 3 that the channel capacity obtained by the beam is not only related to the
demand of the beam but also to the current channel environment of the beam. Through the comparison
of beam 3, beam 4 and beam 5, it can be found that when the service requirements of the beams are
the same but the channel environments are different, the three algorithms assign higher channel
capacity to the beams with better channel environments. When the channel environment of the beams
are the same, but the service demands are different, the three algorithms will allocate the larger
channel capacity to the beam with the larger service demand.
Channel capacity obtained by each beam（Mbit/s）
Figure 3. Channel capacity obtained by each point beam under three distribution modes.
Comparing the three algorithms, the beam allocation capacity of the proposed algorithm is higher for
the beam with better channel conditions and higher service demand than the other two algorithms. In
other words, under the proposed algorithm, the beam with lower service demand can obtain smaller
capacity, the beam with higher service demand can obtain larger capacity similarly. It is more in line
with the real situation of the communication.
The total system amount of each algorithm is calculated as equation (16). Total channel capacity of
different algorithms are shown in figure 4. Compared with the two traditional algorithms, FTPA
improves the total capacity of the system to a certain extent.
6CCME 2020 IOP Publishing
Journal of Physics: Conference Series 1815 (2021) 012026 doi:10.1088/1742-6596/1815/1/012026
Figure 4. Total Channel Capacity of Different Algorithms.
Error between beam demand and actual distribution capacity of each algorithm is calculated as
equation (17). Figure 5 shows the error value between the demand for each beam and the allocation of
the system under different algorithms. It can be seen that because the channel condition of beam 5 is
poor, FTPA allocates less resources for it during allocation. Therefore, the error value is relatively
large. For most other beams, the error value of the FTPA is smaller than that of the traditional two
algorithms, which improves the fairness of system resource allocation to a certain extent.
The error value between the demand and allocation of each beam（1e16）
Figure 5. Error values between beam demand and actual distribution capacity under different
algorithms.
Figure 6. Three algorithms accumulate error values.
Figure 6 shows the accumulated error values of the three algorithms. We can get the following
conclusion: the cumulative error value of FTPA is smaller than the other two algorithms, which proves
that the algorithm makes the resource allocation of the system more balanced and reasonable.
7CCME 2020 IOP Publishing
Journal of Physics: Conference Series 1815 (2021) 012026 doi:10.1088/1742-6596/1815/1/012026
5. Conclusion
Aiming at the resource allocation problem of multi-beam satellite communication system, this paper
proposes the FTPA algorithm considering interference and considering the channel environment of the
beam. Using the total capacity of the system and the fairness of communication as the basis of
evaluation, FTPA is compared with the classical distribution algorithms. It is shown that FTPA
improves the total capacity of the system and the fairness between the beams to a certain extent, and
the utilization ratio of resources is higher and the performance is better.
Acknowledgments
Thanks to the National Natural Science Foundation of China under Grant U1736107 for supporting
this work.
References
[1] Yuanyuan Hu, Gaojun Song 2013 Resource allocation of multi-beam satellite communication in
Ka frequency band Communications technology 000(010):p22-25.
[2] H Han, Y Li and X Dong 2014 Algorithm on joint optimization of power allocation and slot
allocation in satellite communication systems Journal on Communications(26).
[3] Y Hong,A Srinivasan and B Cheng 2008 Optimal power allocation for multiple beam satellite
systems Radio & Wireless Symposium IEEE.
[4] M JJia, X Zhang, and X Gu 2019 Interbeam interference constrained resource allocation for
shared spectrum multibeam satellite communication systems Internet of Things Journal IEEE
6(4) 6052-6059.
[5] L Wang, C Zhang and D Qu 2019 Resource Allocation for Beam-hopping User Downlinks in
Multi-beam Satellite System 2019 15th International Wireless Communications and Mobile
Computing Conference (IWCMC).
[6] J P Choi 2005 Optimum power and beam allocation based on traffic demands and channel
conditions over satellite downlinks IEEE Transactions on Wireless Communications 4(6)
2983-2993.
[7] Dong-Hyun Jung, Min-Su Shin and Joon-Gyu Ryu 2019 Fairness-Based Superframe Design
and Resource Allocation for Dynamic Rate Adaptation in DVB-RCS2 Satellite Systems IEEE
communications letters VOL 23 NO 11.
[8] B Deng, C Jiang and L Kuang 2019 Resource Allocation of Multibeam Communication
Satellite Systems in Sparse Networks ICC 2019 - 2019 IEEE International Conference on
Communications (ICC) IEEE.
[9] A Ivanov, M Stoliarenko and S Kruglik 2019 Dynamic Resource Allocation in LEO Satellite
2019 15th International Wireless Communications and Mobile Computing Conference
(IWCMC).
[10] A J Roumeliotis, C I Kourogiorgas and A D Panagopoulos 2018 Optimal dynamic capacity
allocation for high throughput satellite communications systems IEEE Wireless Communication
Letters PP(99) 1-1.
[11] H Wang, A Liu and X Pan 2014 Optimization of power allocation for multiusers in
multi-spot-beam satellite communication systems Mathematical Problems in Engineering
2014(pt.5) 1-10.
[12] C N Efrem and A D Panagopoulos 2019 Dynamic energy-efficient power allocation in
multibeam satellite systems.
[13] P Zuo, T Peng and W Linghu 2018 Resource allocation for cognitive satellite communications
downlink. IEEE Access 6 75192-75205.
[14] C Han, A Liu and L Huo 2019 A prediction-based resource matching scheme for rentable leo
satellite communication network IEEE Communications Letters PP(99) 1-1.
[15] C N Efrem and A D Panagopoulos 2019 Dynamic energy-efficient power allocation in
multibeam satellite systems.
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